A novel adaptive tracking controller for magnetic levitation systems (MLS) is developed. The controller is based on a special adaptive control scheme incorporating fuzzy model of electromagnetic acceleration enabling fast and accurate fuzzy inversion. The controller ensures accurate tracking of any smooth desired position signal, despite unknown MLS parameters. The closed-loop system stability, in the sense of uniform ultimate boundedness (UUB) of error trajectories, is proved using Lyapunov approach. The closed-loop system performance is investigated during numerical experiments. Finally the proposed controller is verified by successful implementation on a DSP board controlling a typical magnetic levitation ball system. Performed tests and experiments demonstrate that the proposed control technique is robust against discretization and unknown MLS model parameters, provides high tracking accuracy, is easily implementable, and simple to tune.

We present implementation of the proposed adaptive control algorithm working together with a partially identified LuGre friction model to improve performance of linear permanent magnet motor servo. Experimental verification of the system operation is described. We discuss in detail identification of the model parameters, we describe necessary tests and comment on the model accuracy. We give detailed information about control system parameter tuning and we discuss the effect of measurement errors and numerical limitations of a discrete controller on the system performance. We investigate effects of parameter variations on the obtained tracking accuracy for different desired position trajectories.

We consider a two-mass drive with a flexible joint with a nonlinear characteristic of the transmitted torque as a function of the torsion angle. We propose a new, nonlinear, adaptive position-tracking controller, taking this nonlinearity of stiffness into account. The derivation of the controller is based on nonlinear adaptive control theory, incorporates several non-standard mathematical techniques and provides a proof of the uniform ultimate boundedness of tracking errors. As the result, we present a controller that solves the position tracking problem, attenuates dangerous tortional oscillations in the shaft and operates correctly in the presence of unknown torques acting on both sides of the joint, even if all plant parameters are unknown. We demonstrate experimentally that using some materials indeed introduces a nonlinear characteristic of the joint. We prove via real plant experiments that the proposed control algorithm is easily implementable with a DSP controller in real-world applications.

The paper refers to one of the most important problems in industrial automation and robotics—effective motion control in the presence of state variable constraints. A new, nonlinear, adaptive, robust, and practically applicable motion controller for a motor-driven servo is proposed. The developed controller guarantees that the transient of the motion is practically finished in a predefined time, and after this moment, the desired motion trajectory is tracked with specified accuracy, inviolable, time-variable constraints imposed on the position and the velocity are preserved, and all these features are robust against disturbances and violations of the system’s parameters. This approach, distinguished by the fact that the settling time and the quasi-steady-state tracking accuracy are explicitly available design parameters, has never been described before. The controller is based on a special type of time-varying barrier Lyapunov function (BLF), responsible for the finite-time tracking and for meeting the constraints. The derivation of the controller is based on Lyapunov stability theory. A mixture of robust adaptive, nonlinear control techniques is applied to prove the system’s stability. Numerous simulations and experiments with a real permanent-magnet motor- driven servo prove the practical applicability and usefulness of the presented approach.

The proposed modeling technique allows to model disturbances in motion control systems in case of multiple sources of friction. It may be used if the system demonstrates such phenomena as hysteresis, Dahl effect, `frictional memory'. The model will be useful for friction compensation in precise motion control problems, electric drive control, etc. The model will reconstruct accurately the steady-state friction curve, viscous friction, and will approximate complicated dynamics of multisource friction using one internal state variable. The model is based on the fuzzy approximation of the velocity-dependent coefficient influencing the internal state variable dynamics. The remaining parameters are tuned by error minimization. The presented experiments, performed with a real plant, prove the applicability and usefulness of the propose approach.